AI聊天建议影响群体分化与融合，个性化促进分裂，关系型促进多元连接
AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups
- 用大模型提供个性化或关系型消息建议，观察群体互动变化
- 个性化助益者发帖更多且立场更聚集，关系型助益者语言更开放、群体更多元
- 适合关注人机协同社交影响的研究者与政策制定者
群体隔离或凝聚可由微观沟通行为引发，而人工智能辅助的交流可能塑造这一过程。我们报告了一项预注册的在线实验（共557名参与者，60个会话），参与者在多轮中讨论有争议的政治话题，并可自由更换群体。部分参与者获得大型语言模型（LLM）实时消息建议：一种是根据个人立场个性化推荐（个体协助），另一种则整合了群体成员观点（关系协助）。结果发现，微小的AI通信差异会放大为宏观层面的群体结构差异。接受个体协助的参与者发送更多消息且呈现更强的立场聚类；接受关系协助的参与者使用更多接纳性语言，形成更异质化的社会联系。人类与AI共同生成的表达机制可重塑集体组织形态。群体分化与凝聚模式取决于AI如何融入用户交互情境。
原文摘要 · Abstract (English)
Group segregation or cohesion can emerge from micro-level communication, and AI-assisted messaging may shape this process. Here, we report a preregistered online experiment (N = 557 across 60 sessions) in which participants discussed controversial political topics over multiple rounds and could freely change groups. Some participants received real-time message suggestions from a large language model (LLM), either personalized to their stance (individual assistance) or incorporating their group members' perspectives (relational assistance). We find that small variations in AI-mediated communication cascade into macro-level differences in group composition. Participants with individual assistance send more messages and show greater stance-based clustering, whereas those with relational assistance use more receptive language and form more heterogeneous ties. Hybrid expressive processes-jointly produced by humans and AI-can reshape collective organization. The patterns of structural division and cohesion depend on how AI incorporates users' interaction context.
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